Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

Qi Chen; Changjian Shui; Mario Marchand

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arXiv · 2109.14595

Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

Qi Chen·Changjian Shui·Mario Marchand

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Abstract

We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms. Concretely, our analysis proposes a generic understanding of both the conventional learning-to-learn framework and the modern model-agnostic meta-learning (MAML) algorithms. Moreover, we provide a data-dependent generalization bound for a stochastic variant of MAML, which is non-vacuous for deep few-shot learning. As compared to previous bounds that depend on the square norm of gradients, empirical validations on both simulated data and a well-known few-shot benchmark show that our bound is orders of magnitude tighter in most situations.

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Qi Chen, Changjian Shui, Mario Marchand. 2021-09-29. Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. https://arxiv.org/abs/2109.14595

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